Technological innovations in the hardware of RGB-D sensors have allowed the acquisition of 3D point clouds in real time. Consequently, various applications have arisen related to the 3D world, which are receiving increasing attention from researchers. Nevertheless, one of the main problems that remains is the demand for computationally intensive processing that required optimized approaches to deal with 3D vision modeling, especially when it is necessary to perform tasks in real time. A previously proposed multi-resolution 3D model known as foveated point clouds can be a possible solution to this problem. Nevertheless, this is a model limited to a single foveated structure with context dependent mobility. In this work, we propose a new solution for data reduction and feature detection using multifoveation in the point cloud. Nonetheless, the application of several foveated structures results in a considerable increase of processing since there are intersections between regions of distinct structures, which are processed multiple times. Towards solving this problem, the current proposal brings an approach that avoids the processing of redundant regions, which results in even more reduced processing time. Such approach can be used to identify objects in 3D point clouds, one of the key tasks for real-time applications as robotics vision, with efficient synchronization allowing the validation of the model and verification of its applicability in the context of computer vision. Experimental results demonstrate a performance gain of at least 27.21% in processing time while retaining the main features of the original, and maintaining the recognition quality rate in comparison with state-of-the-art 3D object recognition methods.
PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds